中文摘要：

针对现有推荐系统大多基于物品（用户）相似度进行计算,其推荐结果无法兼顾推荐对象的搭配性特征的问题。提出了一种基于联合搭配度的推荐算法框架，该算法框架中的联合搭配度模型，结合了用户交互反馈、物品的文本和结构化知识3方面的信息，分别计算目标物品与候选物品的搭配程度，然后利用逻辑回归算法进行搭配度融合，可以得到与目标物品最相搭配的物品推荐列表．通过在淘宝真实数据集上的实验，该推荐算法框架相比于传统基于相似性的推荐算法，显著提高了搭配推荐的性能曩同时在用户交互记录较少的情况下也能有较好的精确度。

英文摘要：

Since most recommendation systems are based on the calculation of items＇ or users＇ similarity, the results can＇t give consideration to both the complementarity and similarity of recommened objects. An algorthm framework for calculating the joint match degree of items for recommendation systems was proposed. In the framework, combining with the informations of users＇ interaction feedback, items＇ textual knowledge and structural knowledge, the joint match degrees of the target item and those candidate items were calculated respectively. Integrating the match degrees by using logistic regression, a list of items matched with the target item was obtained. Through the experiments on a Taobao real data set, it is indicated that the model significantly improve the performance of recommendation collocation compared to the recommendation algorithm based on similarity only. Moreover, in the situation of fewer users＇ interaction record, the model can also have better accuracy.